{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mlitb-machine-learning-in-the-browser","title":"MLitB: Machine Learning in the Browser","arxiv_id":"1412.2432","date":"2014-12-08","proceeding":null,"authors":["Edward Meeds","Remco Hendriks","Said Al Faraby","Magiel Bruntink","Max Welling"],"abstract":"With few exceptions, the field of Machine Learning (ML) research has largely\nignored the browser as a computational engine. Beyond an educational resource\nfor ML, the browser has vast potential to not only improve the state-of-the-art\nin ML research, but also, inexpensively and on a massive scale, to bring\nsophisticated ML learning and prediction to the public at large. This paper\nintroduces MLitB, a prototype ML framework written entirely in JavaScript,\ncapable of performing large-scale distributed computing with heterogeneous\nclasses of devices. The development of MLitB has been driven by several\nunderlying objectives whose aim is to make ML learning and usage ubiquitous (by\nusing ubiquitous compute devices), cheap and effortlessly distributed, and\ncollaborative. This is achieved by allowing every internet capable device to\nrun training algorithms and predictive models with no software installation and\nby saving models in universally readable formats. Our prototype library is\ncapable of training deep neural networks with synchronized, distributed\nstochastic gradient descent. MLitB offers several important opportunities for\nnovel ML research, including: development of distributed learning algorithms,\nadvancement of web GPU algorithms, novel field and mobile applications, privacy\npreserving computing, and green grid-computing. MLitB is available as open\nsource software.","url_abs":"http://arxiv.org/abs/1412.2432v2","url_pdf":"http://arxiv.org/pdf/1412.2432v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mlitb-machine-learning-in-the-browser","repo_url":"https://github.com/software-engineering-amsterdam/MLitB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.2432","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}